Joint Distance Maps Based Action Recognition with Convolutional Neural Networks

Authors

Chuankun Li, Tianjin University
Yonghong Hou, Tianjin University
Pichao Wang, University of WollongongFollow
Wanqing Li, University of WollongongFollow

RIS ID

113920

Publication Details

Li, C., Hou, Y., Wang, P. & Li, W. (2017). Joint Distance Maps Based Action Recognition with Convolutional Neural Networks. IEEE Signal Processing Letters, 24 (5), 624-628.

Abstract

1994-2012 IEEE.Motivated by the promising performance achieved by deep learning, an effective yet simple method is proposed to encode the spatio-temporal information of skeleton sequences into color texture images, referred to as joint distance maps (JDMs), and convolutional neural networks are employed to exploit the discriminative features from the JDMs for human action and interaction recognition. The pair-wise distances between joints over a sequence of single or multiple person skeletons are encoded into color variations to capture temporal information. The efficacy of the proposed method has been verified by the state-of-the-art results on the large RGB+D Dataset and small UTD-MHAD Dataset in both single-view and cross-view settings.